ML model that is most suited to analyse Google Analytics data
I understand that in your example you are interested in modelling the outcome of the 'result' column.

One easy model I would suggest is to model it using the Bernoulli distribution (https://en.wikipedia.org/wiki/Bernoulli_distribution) with the probability of success p.

Then you can model p with something like this

x = a + b * log(page_views) + c * log(time_spent) + e

p = exp(x) / (1+exp(x))

where e is normally distributed,
e ~ N(0, sigma^2)
(or simply centered around zero).

a, b and c are parameters that you can estimate.

I.e. the probability of success (conversion) is modeled as a sigma function of a certain variable that depends on page_views and time_spend. You can also add squares (and higher powers) of page_views and time_spend in the equation of x (i.e. upto a certain threshold page_views can have a positive effect on conversion and then a negative one, then again a positive effect).

Also reading about logistic regression should put you on the right track: https://en.wikipedia.org/wiki/Logistic_regression